An Approach for Multi-Context-Aware Multi-Criteria Recommender Systems Based on Deep Learning

IEEE Access, Год журнала: 2024, Номер 12, С. 99936 - 99948

Опубликована: Янв. 1, 2024

In an era where digital information is abundant, the role of recommender systems in navigating this vast landscape has become increasingly vital. This study proposes a novel deep learning-based approach integrating multi-context and multi-criteria data within unified neural network framework. The model processes these dimensions concurrently, significantly improving precision personalized recommendations. Context-aware extend traditional two-dimensional user-item preference methods with context awareness multiple criteria. contrast to methods, our intricately weaves together its architecture. concurrent processing enables sophisticated interactions between criteria, enhancing recommendation accuracy. While context-aware incorporate contextual such as time location when making recommendations, multi-criteria-based approaches offer spectrum evaluative enriching user experience more tailored relevant suggestions. Although both have advantages producing accurate referrals, ratings not been employed for Our research multi-context, system address gap. that process separately, learning integration staged; are concurrently processed through facilitates interaction criteria by embedding elements into core network's layers. methodology enhances system's adaptability improves delivering leveraging compounded effects criteria-specific insights. proposed shows superior performance predictive tasks, achieving lowest Mean Absolute Error (MAE) Root Square (RMSE) on TripAdvisor ITMRec datasets compared other state-of-the-art techniques. demonstrate robustness accuracy model.

Язык: Английский

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UBAR: User Behavior-Aware Recommendation with knowledge graph

Knowledge-Based Systems, Год журнала: 2022, Номер 254, С. 109661 - 109661

Опубликована: Авг. 12, 2022

Язык: Английский

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AI-Enabled Trust in Distributed Networks

IEEE Access, Год журнала: 2023, Номер 11, С. 88116 - 88134

Опубликована: Янв. 1, 2023

Cybersecurity, as a crucial aspect of the information society, requires significant attention. Fortunately, concept trust, rooted in sociology, has been studied order to enhance cybersecurity by evaluating trustworthiness nodes with artificial intelligence (AI) techniques distributed networks (DNs). However, scalability issues faced AI-enabled trust hinder its integration DNs. Currently, there is lack comprehensive review article that explores current state development applications. This paper aims address this gap providing state-of-the-art focuses on and how it can be facilitated through AI, particularly utilizing machine learning deep methods. Additionally, provides comparison analysis three key domains field trust: management (TM), intrusion detection systems (IDS), recommender (RS). Some open problems challenges currently exist are manifested, some suggestions for future work presented.

Язык: Английский

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DHSIRS: a novel deep hybrid side information-based recommender system

Multimedia Tools and Applications, Год журнала: 2023, Номер 82(22), С. 34513 - 34539

Опубликована: Март 7, 2023

Язык: Английский

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Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

Journal of Applied Data Sciences, Год журнала: 2023, Номер 4(2), С. 68 - 75

Опубликована: Май 1, 2023

Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, provides users with high quality services achieves accurate marketing, so nowadays resource has pivotal role in modern management. In this paper, I study the algorithm model based on deep learning, taking human an example.

Язык: Английский

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Addressing sparse data challenges in recommendation systems: A Systematic review of rating estimation using sparse rating data and profile enrichment techniques

Intelligent Systems with Applications, Год журнала: 2025, Номер unknown, С. 200474 - 200474

Опубликована: Янв. 1, 2025

Язык: Английский

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A healthy and reliable rating profile expansion approach to address data sparsity in food recommendation systems

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Янв. 20, 2025

Язык: Английский

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Influencer Ranking Framework Using TH-DCNN for influence maximization

Procedia Computer Science, Год журнала: 2025, Номер 252, С. 583 - 592

Опубликована: Янв. 1, 2025

Язык: Английский

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A personalized reinforcement learning recommendation algorithm using bi-clustering techniques

PLoS ONE, Год журнала: 2025, Номер 20(2), С. e0315533 - e0315533

Опубликована: Фев. 20, 2025

Recommender systems have become a core component of various online platforms, helping users get relevant information from the abundant digital data. Traditional RSs often generate static recommendations, which may not adapt well to changing user preferences. To address this problem, we propose novel reinforcement learning (RL) recommendation algorithm that can give personalized recommendations by adapting However, significant drawback RL-based is they are computationally expensive. Moreover, these fail extract local patterns residing within dataset result in generation low quality recommendations. The proposed work utilizes biclustering technique create an efficient environment for RL agents, thus, reducing computation cost and enabling dynamic Additionally, used find locally associated dataset, further improves efficiency agent’s process. experiments eight state-of-the-art algorithms identify appropriate given task. This innovative integration addresses key gaps existing literature. introduced strategy predict item ratings framework. validity evaluated on three datasets movies domain, namely, ML100K, ML-latest-small FilmTrust. These diverse were chosen ensure reliable examination across scenarios. As per nature RL, some specific evaluation metrics like personalization, diversity, intra-list similarity novelty measure diversity investigation motivated need recommender dynamically adjust changes customer Results show our showed promising results when compared with techniques.

Язык: Английский

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Understanding customer loyalty-aware recommender systems in E-commerce: an analytical perspective

Electronic Commerce Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Язык: Английский

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